JezK
Edit File: test_defmatrix.py
import collections.abc import numpy as np from numpy import matrix, asmatrix, bmat from numpy.testing import ( assert_, assert_equal, assert_almost_equal, assert_array_equal, assert_array_almost_equal, assert_raises ) from numpy.linalg import matrix_power from numpy.matrixlib import mat class TestCtor: def test_basic(self): A = np.array([[1, 2], [3, 4]]) mA = matrix(A) assert_(np.all(mA.A == A)) B = bmat("A,A;A,A") C = bmat([[A, A], [A, A]]) D = np.array([[1, 2, 1, 2], [3, 4, 3, 4], [1, 2, 1, 2], [3, 4, 3, 4]]) assert_(np.all(B.A == D)) assert_(np.all(C.A == D)) E = np.array([[5, 6], [7, 8]]) AEresult = matrix([[1, 2, 5, 6], [3, 4, 7, 8]]) assert_(np.all(bmat([A, E]) == AEresult)) vec = np.arange(5) mvec = matrix(vec) assert_(mvec.shape == (1, 5)) def test_exceptions(self): # Check for ValueError when called with invalid string data. assert_raises(ValueError, matrix, "invalid") def test_bmat_nondefault_str(self): A = np.array([[1, 2], [3, 4]]) B = np.array([[5, 6], [7, 8]]) Aresult = np.array([[1, 2, 1, 2], [3, 4, 3, 4], [1, 2, 1, 2], [3, 4, 3, 4]]) mixresult = np.array([[1, 2, 5, 6], [3, 4, 7, 8], [5, 6, 1, 2], [7, 8, 3, 4]]) assert_(np.all(bmat("A,A;A,A") == Aresult)) assert_(np.all(bmat("A,A;A,A", ldict={'A':B}) == Aresult)) assert_raises(TypeError, bmat, "A,A;A,A", gdict={'A':B}) assert_( np.all(bmat("A,A;A,A", ldict={'A':A}, gdict={'A':B}) == Aresult)) b2 = bmat("A,B;C,D", ldict={'A':A,'B':B}, gdict={'C':B,'D':A}) assert_(np.all(b2 == mixresult)) class TestProperties: def test_sum(self): """Test whether matrix.sum(axis=1) preserves orientation. Fails in NumPy <= 0.9.6.2127. """ M = matrix([[1, 2, 0, 0], [3, 4, 0, 0], [1, 2, 1, 2], [3, 4, 3, 4]]) sum0 = matrix([8, 12, 4, 6]) sum1 = matrix([3, 7, 6, 14]).T sumall = 30 assert_array_equal(sum0, M.sum(axis=0)) assert_array_equal(sum1, M.sum(axis=1)) assert_equal(sumall, M.sum()) assert_array_equal(sum0, np.sum(M, axis=0)) assert_array_equal(sum1, np.sum(M, axis=1)) assert_equal(sumall, np.sum(M)) def test_prod(self): x = matrix([[1, 2, 3], [4, 5, 6]]) assert_equal(x.prod(), 720) assert_equal(x.prod(0), matrix([[4, 10, 18]])) assert_equal(x.prod(1), matrix([[6], [120]])) assert_equal(np.prod(x), 720) assert_equal(np.prod(x, axis=0), matrix([[4, 10, 18]])) assert_equal(np.prod(x, axis=1), matrix([[6], [120]])) y = matrix([0, 1, 3]) assert_(y.prod() == 0) def test_max(self): x = matrix([[1, 2, 3], [4, 5, 6]]) assert_equal(x.max(), 6) assert_equal(x.max(0), matrix([[4, 5, 6]])) assert_equal(x.max(1), matrix([[3], [6]])) assert_equal(np.max(x), 6) assert_equal(np.max(x, axis=0), matrix([[4, 5, 6]])) assert_equal(np.max(x, axis=1), matrix([[3], [6]])) def test_min(self): x = matrix([[1, 2, 3], [4, 5, 6]]) assert_equal(x.min(), 1) assert_equal(x.min(0), matrix([[1, 2, 3]])) assert_equal(x.min(1), matrix([[1], [4]])) assert_equal(np.min(x), 1) assert_equal(np.min(x, axis=0), matrix([[1, 2, 3]])) assert_equal(np.min(x, axis=1), matrix([[1], [4]])) def test_ptp(self): x = np.arange(4).reshape((2, 2)) assert_(x.ptp() == 3) assert_(np.all(x.ptp(0) == np.array([2, 2]))) assert_(np.all(x.ptp(1) == np.array([1, 1]))) def test_var(self): x = np.arange(9).reshape((3, 3)) mx = x.view(np.matrix) assert_equal(x.var(ddof=0), mx.var(ddof=0)) assert_equal(x.var(ddof=1), mx.var(ddof=1)) def test_basic(self): import numpy.linalg as linalg A = np.array([[1., 2.], [3., 4.]]) mA = matrix(A) assert_(np.allclose(linalg.inv(A), mA.I)) assert_(np.all(np.array(np.transpose(A) == mA.T))) assert_(np.all(np.array(np.transpose(A) == mA.H))) assert_(np.all(A == mA.A)) B = A + 2j*A mB = matrix(B) assert_(np.allclose(linalg.inv(B), mB.I)) assert_(np.all(np.array(np.transpose(B) == mB.T))) assert_(np.all(np.array(np.transpose(B).conj() == mB.H))) def test_pinv(self): x = matrix(np.arange(6).reshape(2, 3)) xpinv = matrix([[-0.77777778, 0.27777778], [-0.11111111, 0.11111111], [ 0.55555556, -0.05555556]]) assert_almost_equal(x.I, xpinv) def test_comparisons(self): A = np.arange(100).reshape(10, 10) mA = matrix(A) mB = matrix(A) + 0.1 assert_(np.all(mB == A+0.1)) assert_(np.all(mB == matrix(A+0.1))) assert_(not np.any(mB == matrix(A-0.1))) assert_(np.all(mA < mB)) assert_(np.all(mA <= mB)) assert_(np.all(mA <= mA)) assert_(not np.any(mA < mA)) assert_(not np.any(mB < mA)) assert_(np.all(mB >= mA)) assert_(np.all(mB >= mB)) assert_(not np.any(mB > mB)) assert_(np.all(mA == mA)) assert_(not np.any(mA == mB)) assert_(np.all(mB != mA)) assert_(not np.all(abs(mA) > 0)) assert_(np.all(abs(mB > 0))) def test_asmatrix(self): A = np.arange(100).reshape(10, 10) mA = asmatrix(A) A[0, 0] = -10 assert_(A[0, 0] == mA[0, 0]) def test_noaxis(self): A = matrix([[1, 0], [0, 1]]) assert_(A.sum() == matrix(2)) assert_(A.mean() == matrix(0.5)) def test_repr(self): A = matrix([[1, 0], [0, 1]]) assert_(repr(A) == "matrix([[1, 0],\n [0, 1]])") def test_make_bool_matrix_from_str(self): A = matrix('True; True; False') B = matrix([[True], [True], [False]]) assert_array_equal(A, B) class TestCasting: def test_basic(self): A = np.arange(100).reshape(10, 10) mA = matrix(A) mB = mA.copy() O = np.ones((10, 10), np.float64) * 0.1 mB = mB + O assert_(mB.dtype.type == np.float64) assert_(np.all(mA != mB)) assert_(np.all(mB == mA+0.1)) mC = mA.copy() O = np.ones((10, 10), np.complex128) mC = mC * O assert_(mC.dtype.type == np.complex128) assert_(np.all(mA != mB)) class TestAlgebra: def test_basic(self): import numpy.linalg as linalg A = np.array([[1., 2.], [3., 4.]]) mA = matrix(A) B = np.identity(2) for i in range(6): assert_(np.allclose((mA ** i).A, B)) B = np.dot(B, A) Ainv = linalg.inv(A) B = np.identity(2) for i in range(6): assert_(np.allclose((mA ** -i).A, B)) B = np.dot(B, Ainv) assert_(np.allclose((mA * mA).A, np.dot(A, A))) assert_(np.allclose((mA + mA).A, (A + A))) assert_(np.allclose((3*mA).A, (3*A))) mA2 = matrix(A) mA2 *= 3 assert_(np.allclose(mA2.A, 3*A)) def test_pow(self): """Test raising a matrix to an integer power works as expected.""" m = matrix("1. 2.; 3. 4.") m2 = m.copy() m2 **= 2 mi = m.copy() mi **= -1 m4 = m2.copy() m4 **= 2 assert_array_almost_equal(m2, m**2) assert_array_almost_equal(m4, np.dot(m2, m2)) assert_array_almost_equal(np.dot(mi, m), np.eye(2)) def test_scalar_type_pow(self): m = matrix([[1, 2], [3, 4]]) for scalar_t in [np.int8, np.uint8]: two = scalar_t(2) assert_array_almost_equal(m ** 2, m ** two) def test_notimplemented(self): '''Check that 'not implemented' operations produce a failure.''' A = matrix([[1., 2.], [3., 4.]]) # __rpow__ with assert_raises(TypeError): 1.0**A # __mul__ with something not a list, ndarray, tuple, or scalar with assert_raises(TypeError): A*object() class TestMatrixReturn: def test_instance_methods(self): a = matrix([1.0], dtype='f8') methodargs = { 'astype': ('intc',), 'clip': (0.0, 1.0), 'compress': ([1],), 'repeat': (1,), 'reshape': (1,), 'swapaxes': (0, 0), 'dot': np.array([1.0]), } excluded_methods = [ 'argmin', 'choose', 'dump', 'dumps', 'fill', 'getfield', 'getA', 'getA1', 'item', 'nonzero', 'put', 'putmask', 'resize', 'searchsorted', 'setflags', 'setfield', 'sort', 'partition', 'argpartition', 'take', 'tofile', 'tolist', 'tostring', 'tobytes', 'all', 'any', 'sum', 'argmax', 'argmin', 'min', 'max', 'mean', 'var', 'ptp', 'prod', 'std', 'ctypes', 'itemset', ] for attrib in dir(a): if attrib.startswith('_') or attrib in excluded_methods: continue f = getattr(a, attrib) if isinstance(f, collections.abc.Callable): # reset contents of a a.astype('f8') a.fill(1.0) if attrib in methodargs: args = methodargs[attrib] else: args = () b = f(*args) assert_(type(b) is matrix, "%s" % attrib) assert_(type(a.real) is matrix) assert_(type(a.imag) is matrix) c, d = matrix([0.0]).nonzero() assert_(type(c) is np.ndarray) assert_(type(d) is np.ndarray) class TestIndexing: def test_basic(self): x = asmatrix(np.zeros((3, 2), float)) y = np.zeros((3, 1), float) y[:, 0] = [0.8, 0.2, 0.3] x[:, 1] = y > 0.5 assert_equal(x, [[0, 1], [0, 0], [0, 0]]) class TestNewScalarIndexing: a = matrix([[1, 2], [3, 4]]) def test_dimesions(self): a = self.a x = a[0] assert_equal(x.ndim, 2) def test_array_from_matrix_list(self): a = self.a x = np.array([a, a]) assert_equal(x.shape, [2, 2, 2]) def test_array_to_list(self): a = self.a assert_equal(a.tolist(), [[1, 2], [3, 4]]) def test_fancy_indexing(self): a = self.a x = a[1, [0, 1, 0]] assert_(isinstance(x, matrix)) assert_equal(x, matrix([[3, 4, 3]])) x = a[[1, 0]] assert_(isinstance(x, matrix)) assert_equal(x, matrix([[3, 4], [1, 2]])) x = a[[[1], [0]], [[1, 0], [0, 1]]] assert_(isinstance(x, matrix)) assert_equal(x, matrix([[4, 3], [1, 2]])) def test_matrix_element(self): x = matrix([[1, 2, 3], [4, 5, 6]]) assert_equal(x[0][0], matrix([[1, 2, 3]])) assert_equal(x[0][0].shape, (1, 3)) assert_equal(x[0].shape, (1, 3)) assert_equal(x[:, 0].shape, (2, 1)) x = matrix(0) assert_equal(x[0, 0], 0) assert_equal(x[0], 0) assert_equal(x[:, 0].shape, x.shape) def test_scalar_indexing(self): x = asmatrix(np.zeros((3, 2), float)) assert_equal(x[0, 0], x[0][0]) def test_row_column_indexing(self): x = asmatrix(np.eye(2)) assert_array_equal(x[0,:], [[1, 0]]) assert_array_equal(x[1,:], [[0, 1]]) assert_array_equal(x[:, 0], [[1], [0]]) assert_array_equal(x[:, 1], [[0], [1]]) def test_boolean_indexing(self): A = np.arange(6) A.shape = (3, 2) x = asmatrix(A) assert_array_equal(x[:, np.array([True, False])], x[:, 0]) assert_array_equal(x[np.array([True, False, False]),:], x[0,:]) def test_list_indexing(self): A = np.arange(6) A.shape = (3, 2) x = asmatrix(A) assert_array_equal(x[:, [1, 0]], x[:, ::-1]) assert_array_equal(x[[2, 1, 0],:], x[::-1,:]) class TestPower: def test_returntype(self): a = np.array([[0, 1], [0, 0]]) assert_(type(matrix_power(a, 2)) is np.ndarray) a = mat(a) assert_(type(matrix_power(a, 2)) is matrix) def test_list(self): assert_array_equal(matrix_power([[0, 1], [0, 0]], 2), [[0, 0], [0, 0]]) class TestShape: a = np.array([[1], [2]]) m = matrix([[1], [2]]) def test_shape(self): assert_equal(self.a.shape, (2, 1)) assert_equal(self.m.shape, (2, 1)) def test_numpy_ravel(self): assert_equal(np.ravel(self.a).shape, (2,)) assert_equal(np.ravel(self.m).shape, (2,)) def test_member_ravel(self): assert_equal(self.a.ravel().shape, (2,)) assert_equal(self.m.ravel().shape, (1, 2)) def test_member_flatten(self): assert_equal(self.a.flatten().shape, (2,)) assert_equal(self.m.flatten().shape, (1, 2)) def test_numpy_ravel_order(self): x = np.array([[1, 2, 3], [4, 5, 6]]) assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6]) assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6]) assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6]) assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6]) x = matrix([[1, 2, 3], [4, 5, 6]]) assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6]) assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6]) assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6]) assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6]) def test_matrix_ravel_order(self): x = matrix([[1, 2, 3], [4, 5, 6]]) assert_equal(x.ravel(), [[1, 2, 3, 4, 5, 6]]) assert_equal(x.ravel(order='F'), [[1, 4, 2, 5, 3, 6]]) assert_equal(x.T.ravel(), [[1, 4, 2, 5, 3, 6]]) assert_equal(x.T.ravel(order='A'), [[1, 2, 3, 4, 5, 6]]) def test_array_memory_sharing(self): assert_(np.may_share_memory(self.a, self.a.ravel())) assert_(not np.may_share_memory(self.a, self.a.flatten())) def test_matrix_memory_sharing(self): assert_(np.may_share_memory(self.m, self.m.ravel())) assert_(not np.may_share_memory(self.m, self.m.flatten())) def test_expand_dims_matrix(self): # matrices are always 2d - so expand_dims only makes sense when the # type is changed away from matrix. a = np.arange(10).reshape((2, 5)).view(np.matrix) expanded = np.expand_dims(a, axis=1) assert_equal(expanded.ndim, 3) assert_(not isinstance(expanded, np.matrix))